The purpose of this analysis is to build a model to identify medical payment types
This tesk is an aggregated workflow to build a ml process, more like in real cases
The analysis including 3 parts
## data processing
import pandas as pd
import numpy as np
import csv
import random
import os
#plotting
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
#preprocessing
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
#pipeline
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
#model selection
from sklearn.model_selection import GridSearchCV,RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
#model
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
import xgboost as xgb
import graphviz
# model evaluation
from sklearn.metrics import precision_recall_curve, roc_curve
from sklearn.metrics import r2_score
import datetime
total_gen_row=11239734#sum(1 for i in open('OP_DTL_GNRL_PGYR2017_P06282019.csv'))
total_res_row=653489#sum(1 for i in open('OP_DTL_RSRCH_PGYR2017_P06282019.csv'))
total_gen_row,total_res_row
#(11239734, 653489)
nsamples_res=200000
skiprows_gen = np.sort(np.random.choice(range(1, total_gen_row), replace = False, size = total_gen_row - nsamples_res))
skiprows_res = np.sort(np.random.choice(range(1, total_res_row), replace = False, size = total_res_row - nsamples_res))
gen = pd.read_csv("OP_DTL_GNRL_PGYR2017_P06282019.csv", skiprows = skiprows_gen, parse_dates=['Date_of_Payment'])
res = pd.read_csv("OP_DTL_RSRCH_PGYR2017_P06282019.csv", skiprows = skiprows_res, parse_dates=['Date_of_Payment'])
C:\Users\Yilun\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (16,17,21,22,23,24,67,72) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result) C:\Users\Yilun\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (2,5,7,8,9,10,17,18,19,20,21,22,23,24,37,38,44,45,50,52,62,67,68,69,70,71,72,73,74,75,76,79,80,81,87,88,89,90,91,92,93,94,95,96,99,100,101,107,108,109,110,111,112,113,114,115,116,119,120,121,151,156,161,162,163) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)
gen.to_csv('gen.csv')
res.to_csv('res.csv')
gen=pd.read_csv('gen.csv')
res=pd.read_csv('res.csv')
C:\Users\Yilun\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (17,18,22,23,24,25,68,73) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result) C:\Users\Yilun\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (3,6,8,9,10,11,18,19,20,21,22,23,24,25,38,39,45,46,51,53,63,68,69,70,71,72,73,74,75,76,77,80,81,82,88,89,90,91,92,93,94,95,96,97,100,101,102,108,109,110,111,112,113,114,115,116,117,120,121,122,152,157,162,163,164) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)
warnings show mixed types of data in multiple columns, which is a sign to
1. check the null and missing value
2. check whether some numerical value is converted into categorical or vice versa
gen.shape,res.shape
((199999, 75), (199999, 176))
Feature could be grouped by feature name at the first glance
gen['Tag']=0
res['Tag']=1
#combine 2 sets of data with different result to 1, leaving columns that are in common in both sets
#the common features are 66
comb=pd.concat([gen,res],axis=0,verify_integrity=False,sort=True,join="inner")
comb.shape
comb.shape
(399998, 66)
Create a table showing each variable''s missing ratio and unique value
#unique check
unique=pd.DataFrame(comb.groupby('Tag').nunique())
unique_dfT=unique.T
unique_dfT['Overall_Unique']=comb.nunique()
unique_dfT.columns=['0_unique','1_unique','Overall_Unique']
#null check
def null_check(x):
return sum(x.isnull())
nulldf=comb.groupby('Tag').agg(null_check)
nulldfT=nulldf.T
nulldfT.rename(columns={0: "0_Null", 1: "1_Null"},inplace=True)
nulldfT['Overall_Null_Ratio'] =nulldfT.apply(lambda x:sum(x[:2]),axis=1)/400000
nulldfT['0_Null_Ratio'] =nulldfT['0_Null']/200000
nulldfT['1_Null_Ratio'] =nulldfT['1_Null']/200000
dfinfo=pd.concat([nulldfT,unique_dfT],axis=1)
dfinfo.shape
pd.set_option('max_colwidth', 100)
pd.set_option('display.max_rows', 500)
dfinfo
C:\Users\Yilun\Anaconda3\lib\site-packages\ipykernel_launcher.py:19: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=False'. To retain the current behavior and silence the warning, pass 'sort=True'.
pd.set_option('max_colwidth', 100)
pd.set_option('display.max_rows', 500)
dfinfo
| 0_Null | 1_Null | Overall_Null_Ratio | 0_Null_Ratio | 1_Null_Ratio | 0_unique | 1_unique | Overall_Unique | |
|---|---|---|---|---|---|---|---|---|
| Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 20 | 18 | 24 |
| Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 1009 | 562 | 1126 |
| Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Name | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 1022 | 569 | 1155 |
| Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State | 1738.0 | 19704.0 | 0.053605 | 0.008690 | 0.098520 | 42 | 35 | 44 |
| Associated_Drug_or_Biological_NDC_1 | 53897.0 | 102822.0 | 0.391798 | 0.269485 | 0.514110 | 925 | 658 | 1111 |
| Associated_Drug_or_Biological_NDC_2 | 161457.0 | 196124.0 | 0.893953 | 0.807285 | 0.980620 | 400 | 35 | 415 |
| Associated_Drug_or_Biological_NDC_3 | 186352.0 | 199578.0 | 0.964825 | 0.931760 | 0.997890 | 258 | 14 | 268 |
| Associated_Drug_or_Biological_NDC_4 | 196669.0 | 199902.0 | 0.991428 | 0.983345 | 0.999510 | 132 | 6 | 136 |
| Associated_Drug_or_Biological_NDC_5 | 199511.0 | 199949.0 | 0.998650 | 0.997555 | 0.999745 | 46 | 4 | 50 |
| Change_Type | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 4 | 4 | 4 |
| Covered_Recipient_Type | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 2 | 4 | 4 |
| Covered_or_Noncovered_Indicator_1 | 9414.0 | 26005.0 | 0.088548 | 0.047070 | 0.130025 | 2 | 2 | 2 |
| Covered_or_Noncovered_Indicator_2 | 155646.0 | 188880.0 | 0.861315 | 0.778230 | 0.944400 | 2 | 2 | 2 |
| Covered_or_Noncovered_Indicator_3 | 183198.0 | 198320.0 | 0.953795 | 0.915990 | 0.991600 | 2 | 2 | 2 |
| Covered_or_Noncovered_Indicator_4 | 195285.0 | 199324.0 | 0.986522 | 0.976425 | 0.996620 | 2 | 2 | 2 |
| Covered_or_Noncovered_Indicator_5 | 198651.0 | 199473.0 | 0.995310 | 0.993255 | 0.997365 | 2 | 2 | 2 |
| Date_of_Payment | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 365 | 364 | 365 |
| Delay_in_Publication_Indicator | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 1 | 1 | 1 |
| Dispute_Status_for_Publication | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 2 | 2 | 2 |
| Form_of_Payment_or_Transfer_of_Value | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 5 | 2 | 5 |
| Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 | 13464.0 | 58780.0 | 0.180610 | 0.067320 | 0.293900 | 4 | 4 | 4 |
| Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_2 | 156493.0 | 190249.0 | 0.866855 | 0.782465 | 0.951245 | 4 | 4 | 4 |
| Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_3 | 183562.0 | 198321.0 | 0.954708 | 0.917810 | 0.991605 | 4 | 3 | 4 |
| Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_4 | 195356.0 | 199324.0 | 0.986700 | 0.976780 | 0.996620 | 4 | 4 | 4 |
| Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_5 | 198677.0 | 199473.0 | 0.995375 | 0.993385 | 0.997365 | 4 | 4 | 4 |
| Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1 | 13942.0 | 59794.0 | 0.184340 | 0.069710 | 0.298970 | 4203 | 1821 | 5026 |
| Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_2 | 156473.0 | 190250.0 | 0.866807 | 0.782365 | 0.951250 | 1227 | 140 | 1306 |
| Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_3 | 183536.0 | 198321.0 | 0.954642 | 0.917680 | 0.991605 | 810 | 78 | 866 |
| Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_4 | 195328.0 | 199324.0 | 0.986630 | 0.976640 | 0.996620 | 408 | 41 | 439 |
| Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_5 | 198667.0 | 199473.0 | 0.995350 | 0.993335 | 0.997365 | 167 | 28 | 186 |
| Payment_Publication_Date | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 1 | 1 | 1 |
| Physician_First_Name | 765.0 | 191181.0 | 0.479865 | 0.003825 | 0.955905 | 19982 | 1493 | 20205 |
| Physician_Last_Name | 772.0 | 191181.0 | 0.479882 | 0.003860 | 0.955905 | 57823 | 3053 | 58721 |
| Physician_License_State_code1 | 767.0 | 191181.0 | 0.479870 | 0.003835 | 0.955905 | 55 | 51 | 55 |
| Physician_License_State_code2 | 196007.0 | 199739.0 | 0.989365 | 0.980035 | 0.998695 | 51 | 31 | 51 |
| Physician_License_State_code3 | 199091.0 | 199926.0 | 0.997542 | 0.995455 | 0.999630 | 47 | 15 | 47 |
| Physician_License_State_code4 | 199847.0 | 199985.0 | 0.999580 | 0.999235 | 0.999925 | 29 | 6 | 29 |
| Physician_License_State_code5 | 199971.0 | 199999.0 | 0.999925 | 0.999855 | 0.999995 | 15 | 0 | 15 |
| Physician_Middle_Name | 82333.0 | 194099.0 | 0.691080 | 0.411665 | 0.970495 | 8659 | 470 | 8764 |
| Physician_Name_Suffix | 194740.0 | 199743.0 | 0.986208 | 0.973700 | 0.998715 | 34 | 13 | 35 |
| Physician_Primary_Type | 765.0 | 191181.0 | 0.479865 | 0.003825 | 0.955905 | 6 | 5 | 6 |
| Physician_Profile_ID | 765.0 | 191181.0 | 0.479865 | 0.003825 | 0.955905 | 117471 | 3580 | 119547 |
| Physician_Specialty | 1033.0 | 191183.0 | 0.480540 | 0.005165 | 0.955915 | 300 | 156 | 303 |
| Product_Category_or_Therapeutic_Area_1 | 14056.0 | 68480.0 | 0.206340 | 0.070280 | 0.342400 | 1085 | 623 | 1233 |
| Product_Category_or_Therapeutic_Area_2 | 156576.0 | 190251.0 | 0.867067 | 0.782880 | 0.951255 | 363 | 78 | 390 |
| Product_Category_or_Therapeutic_Area_3 | 183637.0 | 198321.0 | 0.954895 | 0.918185 | 0.991605 | 274 | 46 | 295 |
| Product_Category_or_Therapeutic_Area_4 | 195404.0 | 199324.0 | 0.986820 | 0.977020 | 0.996620 | 178 | 25 | 190 |
| Product_Category_or_Therapeutic_Area_5 | 198718.0 | 199473.0 | 0.995478 | 0.993590 | 0.997365 | 91 | 16 | 101 |
| Program_Year | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 1 | 1 | 1 |
| Recipient_City | 1.0 | 196.0 | 0.000492 | 0.000005 | 0.000980 | 9383 | 3691 | 9931 |
| Recipient_Country | 0.0 | 196.0 | 0.000490 | 0.000000 | 0.000980 | 4 | 8 | 9 |
| Recipient_Postal_Code | 199994.0 | 199825.0 | 0.999548 | 0.999970 | 0.999125 | 5 | 20 | 25 |
| Recipient_Primary_Business_Street_Address_Line1 | 0.0 | 196.0 | 0.000490 | 0.000000 | 0.000980 | 93345 | 27333 | 114326 |
| Recipient_Primary_Business_Street_Address_Line2 | 128304.0 | 146114.0 | 0.686045 | 0.641520 | 0.730570 | 13225 | 5912 | 17576 |
| Recipient_Province | 199994.0 | 199885.0 | 0.999698 | 0.999970 | 0.999425 | 5 | 9 | 14 |
| Recipient_State | 5.0 | 370.0 | 0.000937 | 0.000025 | 0.001850 | 57 | 54 | 57 |
| Recipient_Zip_Code | 5.0 | 370.0 | 0.000937 | 0.000025 | 0.001850 | 51021 | 9824 | 54811 |
| Record_ID | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 199999 | 199999 | 399998 |
| Related_Product_Indicator | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 2 | 2 | 2 |
| Submitting_Applicable_Manufacturer_or_Applicable_GPO_Name | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 915 | 498 | 1016 |
| Tag | NaN | NaN | NaN | NaN | NaN | 1 | 1 | 2 |
| Teaching_Hospital_CCN | 199234.0 | 168058.0 | 0.918230 | 0.996170 | 0.840290 | 372 | 634 | 721 |
| Teaching_Hospital_ID | 199234.0 | 168058.0 | 0.918230 | 0.996170 | 0.840290 | 372 | 634 | 721 |
| Teaching_Hospital_Name | 199234.0 | 168058.0 | 0.918230 | 0.996170 | 0.840290 | 383 | 862 | 959 |
| Total_Amount_of_Payment_USDollars | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 19894 | 62898 | 74533 |
Criteria to drop feature
# quick func to check the top in categorical data
def top_cat(X):
l1=comb[comb['Tag']==0][X].value_counts()[:10]
l2=comb[comb['Tag']==1][X].value_counts()[:10]
print('gen')
print(l1)
print('res')
print(l2)
print('In common')
print(len(set(l1.index).intersection(set(l2.index))))
top_cat("Associated_Drug_or_Biological_NDC_1")
gen 50458-580-30 5106 0169-4060-90 4537 00031-062-10 3713 0078-0659-20 3097 55513-710-01 2712 50458-140-30 2655 0006-0277-82 2408 0002-1433-80 2205 00597-0152-0 2194 0074-4339-02 2173 Name: Associated_Drug_or_Biological_NDC_1, dtype: int64 res 0006-3029-01 9569 62856-529-60 8506 64406-006-02 3553 0597-0140-30 2828 0169-2660-15 2751 65757-300-01 2083 0597-0152-30 1914 0069-0187-21 1788 0024-5901-00 1718 62856-710-30 1570 Name: Associated_Drug_or_Biological_NDC_1, dtype: int64 In common 0
this shows this metric highly likely leaking information or providing really distinguishable value
Considering there are overlap between the 2, we might want to add it back
top_cat("Covered_or_Noncovered_Indicator_1")
gen Covered 185343 Non-Covered 5078 Name: Covered_or_Noncovered_Indicator_1, dtype: int64 res Covered 125299 Non-Covered 48416 Name: Covered_or_Noncovered_Indicator_1, dtype: int64 In common 2
top_cat("Product_Category_or_Therapeutic_Area_1")
gen Diabetes 10129 RESPIRATORY 9173 Cardiovascular & Metabolism 7845 Immunology 6088 CARDIOVASCULAR 5757 NEUROSCIENCE 5246 Endocrinology 5144 Cardiovascular and Metabolism 4788 GASTROENTEROLOGY 4546 DIABETES 4251 Name: Product_Category_or_Therapeutic_Area_1, dtype: int64 res ONCOLOGY 17976 Nutrition and Weight Loss 8506 Oncology 7939 DIABETES 7699 Diabetes 6411 Cardiology/Vascular Diseases 5854 NEUROLOGY 5851 CNS 3767 Immunology 3748 CARDIOVASCULAR 3553 Name: Product_Category_or_Therapeutic_Area_1, dtype: int64 In common 4
The above 2 is normal, no need to drop
build a filter of thefinal columns
rule on missing value
dfinfo.columns
Index(['0_Null', '1_Null', 'Overall_Null_Ratio', '0_Null_Ratio',
'1_Null_Ratio', '0_unique', '1_unique', 'Overall_Unique'],
dtype='object')
dfinfo_1=dfinfo[(dfinfo['0_Null_Ratio']<0.8)&(dfinfo['1_Null_Ratio']<0.8)&(dfinfo['Overall_Null_Ratio']<0.6)&(dfinfo['Overall_Unique']>1)&(dfinfo['Overall_Unique']<399997)]
var_set_1=dfinfo_1.index
comb.loc[:,var_set_1].dtypes
Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country object Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID int64 Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Name object Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State object Associated_Drug_or_Biological_NDC_1 object Change_Type object Covered_Recipient_Type object Covered_or_Noncovered_Indicator_1 object Date_of_Payment datetime64[ns] Dispute_Status_for_Publication object Form_of_Payment_or_Transfer_of_Value object Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 object Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1 object Product_Category_or_Therapeutic_Area_1 object Recipient_City object Recipient_Country object Recipient_Primary_Business_Street_Address_Line1 object Recipient_State object Recipient_Zip_Code object Related_Product_Indicator object Submitting_Applicable_Manufacturer_or_Applicable_GPO_Name object Total_Amount_of_Payment_USDollars float64 dtype: object
top_cat("Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID")
gen 100000000286 10537 100000000278 8937 100000000232 8504 100000000146 7886 100000000144 7179 100000000053 6955 100000000203 6147 100000005449 5931 100000000228 5845 100000000234 5754 Name: Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID, dtype: int64 res 100000000286 14400 100000000053 13319 100000000263 12007 100000000136 10836 100000000255 8724 100000000067 8723 100000000204 7754 100000000163 6681 100000000193 5899 100000010579 5474 Name: Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID, dtype: int64 In common 2
top_cat("Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1")
gen XARELTO 5112 Victoza 4537 FARXIGA 3713 ENTRESTO 3093 ELIQUIS 3024 JARDIANCE 2733 Prolia 2712 INVOKANA 2665 JANUVIA 2408 TRULICITY 2205 Name: Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1, dtype: int64 res KEYTRUDA 9569 Belviq 8506 TECFIDERA 3553 TRADJENTA 2828 Tresiba 2751 Non-Covered Product 2508 Sentus 2216 Vivitrol 380mg 2083 SAR231893 1950 JARDIANCE 1851 Name: Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1, dtype: int64 In common 1
# except for amount, drop those cat var who has more than 100 cont
var_set_2=dfinfo_1[(dfinfo_1['Overall_Unique']<100)].index
var_2=var_set_2.tolist()
var_2.append('Total_Amount_of_Payment_USDollars')
#first check the cont var : payment dollar
plt.hist(np.log(comb.Total_Amount_of_Payment_USDollars))
(array([7.50000e+01, 1.64100e+03, 2.25130e+04, 1.65282e+05, 7.36010e+04,
8.51800e+04, 4.22620e+04, 8.77900e+03, 6.31000e+02, 3.40000e+01]),
array([-4.60517019, -2.48019474, -0.3552193 , 1.76975614, 3.89473159,
6.01970703, 8.14468247, 10.26965792, 12.39463336, 14.5196088 ,
16.64458425]),
<a list of 10 Patch objects>)
comb.groupby('Tag').mean()['Total_Amount_of_Payment_USDollars']
Tag 0 311.429872 1 7634.153894 Name: Total_Amount_of_Payment_USDollars, dtype: float64
plt.scatter(np.log(comb.Total_Amount_of_Payment_USDollars),comb.Tag,alpha=0.2)
<matplotlib.collections.PathCollection at 0x1969f3a8080>
# then check categorical variables
fig, ax = plt.subplots( len(var_2[:-1]), figsize = (20, len(var_2[:-1])*5))
i=0
for v in var_2[:-1]:
sns.countplot(v, hue='Tag', data = comb, ax = ax[i])
i+=1
plt.tight_layout()
change type is a highly likely variable to be leaking result need to be dropped
var_2
['Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country', 'Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State', 'Change_Type', 'Covered_or_Noncovered_Indicator_1', 'Dispute_Status_for_Publication', 'Form_of_Payment_or_Transfer_of_Value', 'Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1', 'Recipient_Country', 'Recipient_State', 'Related_Product_Indicator', 'Total_Amount_of_Payment_USDollars']
var_2.remove('Covered_Recipient_Type')
comb_reg=comb.copy()[var_2]
comb_reg.fillna('NA',inplace=True)
X_trainval,X_test,y_trainval,y_test=train_test_split(comb_reg,comb['Tag'])
X_train,X_val,y_train,y_val=train_test_split(X_trainval,y_trainval)
cat=comb_reg.dtypes=='object'
cont=comb_reg.dtypes!='object'
prep = ColumnTransformer([("OneHot", OneHotEncoder(handle_unknown='ignore'),cat), ('pt', 'passthrough',cont)])
log = Pipeline([('preprocessing', prep), ('LogReg',LogisticRegression())])
log.fit(X_train,y_train)
#LogReg seems to have a fair result. Check the actual parameter for the model -- which variable plays a more important role
log.score(X_train,y_train)
cols = log.named_steps['preprocessing'].named_transformers_['OneHot'].get_feature_names().tolist() \
+ ['Total_Amount_of_Payment_USDollars']
fig, ax = plt.subplots(1, 1, figsize=(25, 8))
plt.xticks(rotation='vertical')
sns.barplot(x=cols, y=log.named_steps['LogReg'].coef_[0])
plt.show()
# second model is tree
tree = Pipeline([('preprocessing', prep), ('Tree',DecisionTreeClassifier(
min_impurity_decrease=0.03,
min_samples_leaf=400, min_samples_split=400,
min_weight_fraction_leaf=0.01,))])
tree.fit(X_train,y_train)
print("{0} training accuracy: {1:.3f} and test accuracy: {2:.3f}".\
format('tree',\
tree.score(X_train,y_train),tree.score(X_test,y_test)))
tree_dot = export_graphviz(tree.named_steps['Tree'], out_file=None, feature_names=cols)
graph = graphviz.Source(tree_dot)
graph.render(cleanup=True)
graph
tree = Pipeline([('preprocessing', prep), ('Tree',DecisionTreeClassifier(
min_impurity_decrease=0.01,
min_samples_leaf=20, min_samples_split=40,
min_weight_fraction_leaf=0.0001,))])
tree.fit(X_train,y_train)
print("{0} training accuracy: {1:.3f} and test accuracy: {2:.3f}".\
format('tree',\
tree.score(X_train,y_train),tree.score(X_test,y_test)))
tree_dot = export_graphviz(tree.named_steps['Tree'], out_file=None, feature_names=cols)
graph = graphviz.Source(tree_dot)
graph.render(cleanup=True)
graph
tree training accuracy: 0.878 and test accuracy: 0.877
fpr_log, tpr_log, thresholds_log = roc_curve(y_test, log.predict_proba(X_test)[:,1])
fpr_tree, tpr_tree, thresholds_tree = roc_curve(y_test, tree.predict_proba(X_test)[:,1])
plt.plot(fpr_tree, tpr_tree, label = 'Decision Tree')
plt.plot(fpr_log, tpr_log, label = 'Log')
plt.xlabel("FPR")
plt.ylabel("TPR")
plt.title("ROC Curve for Tree and Log")
plt.legend()
plt.show()
comb_reg.reset_index(inplace=True)
comb_reg.drop('index',axis=1,inplace=True)
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-93-16eacaf864e6> in <module> ----> 1 comb_reg.reset_index(inplace=True) ~\Anaconda3\lib\site-packages\pandas\core\frame.py in reset_index(self, level, drop, inplace, col_level, col_fill) 4707 # to ndarray and maybe infer different dtype 4708 level_values = _maybe_casted_values(lev, lab) -> 4709 new_obj.insert(0, name, level_values) 4710 4711 new_obj.index = new_index ~\Anaconda3\lib\site-packages\pandas\core\frame.py in insert(self, loc, column, value, allow_duplicates) 3589 self._ensure_valid_index(value) 3590 value = self._sanitize_column(column, value, broadcast=False) -> 3591 self._data.insert(loc, column, value, allow_duplicates=allow_duplicates) 3592 3593 def assign(self, **kwargs): ~\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in insert(self, loc, item, value, allow_duplicates) 1171 if not allow_duplicates and item in self.items: 1172 # Should this be a different kind of error?? -> 1173 raise ValueError("cannot insert {}, already exists".format(item)) 1174 1175 if not isinstance(loc, int): ValueError: cannot insert level_0, already exists
comb_reg.columns
Index(['level_0', 'index',
'Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country',
'Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State',
'Change_Type', 'Covered_or_Noncovered_Indicator_1',
'Dispute_Status_for_Publication',
'Form_of_Payment_or_Transfer_of_Value',
'Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1',
'Recipient_Country', 'Recipient_State', 'Related_Product_Indicator',
'Total_Amount_of_Payment_USDollars'],
dtype='object')
top_cat("Associated_Drug_or_Biological_NDC_1")
gen 50458-580-30 4988 0169-4060-90 4756 00031-062-10 3789 0078-0659-20 3189 50458-140-30 2724 55513-710-01 2724 0002-1433-80 2294 0006-0277-82 2244 00597-0152-0 2229 0074-4339-02 2157 Name: Associated_Drug_or_Biological_NDC_1, dtype: int64 res 0006-3029-01 9568 62856-529-60 8493 64406-006-02 3538 0597-0140-30 2782 0169-2660-15 2732 65757-300-01 2109 0597-0152-30 1920 0069-0187-21 1788 0024-5901-00 1755 62856-710-30 1645 Name: Associated_Drug_or_Biological_NDC_1, dtype: int64 In common 0
#break them down and check the pattern
s=[]
for i in comb[ "Associated_Drug_or_Biological_NDC_1"].fillna('0-0-0'):
s.append(i.split('-'))
NDC_1_DF=pd.DataFrame(s,columns=['dig1','dig2','dig3'])
comb_modify=pd.concat([comb_reg,NDC_1_DF],axis=1)
for i in ['dig1','dig2','dig3']:
l=comb_modify[i].value_counts()[:10].index.tolist()
comb_modify[i] = np.where([i in l for i in comb_modify[i]], comb_modify[i],'0')
for this variable, we want to break down into 3 digits and leave the top 10 unchanged for each one
check Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID and leave top 10 category
l=comb.Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID.value_counts()[:20]
l.index
[i in l.index for i in comb['Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID'] ]
comb_modify['Applicable_Manufacturer_or_Applicable_GPO_M']=\
np.where([i in l.index for i in comb['Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID'] ],\
comb['Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_ID'],'000000000000')
CHECK DATE OF PAYMENT
comb_modify['Payment_Weekday']=\
pd.to_datetime(comb['Date_of_Payment']).apply(lambda x:str(x.weekday())).tolist()
comb_modify['Payment_Month']=\
pd.to_datetime(comb['Date_of_Payment']).apply(lambda x:str(x.month)).tolist()
Check name of drug
comb['Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1'].value_counts()
KEYTRUDA 9995
Belviq 8641
Victoza 5820
XARELTO 5621
JARDIANCE 4614
...
Oryx and Lotus 1
Provox Laryngectomy Pulmonary Kit 1
SELF-CATH 1
SHIFT 1
RECOMBIVAX HIB 1
Name: Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1, Length: 5026, dtype: int64
top_cat('Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1')
gen XARELTO 4998 Victoza 4756 FARXIGA 3789 ENTRESTO 3184 ELIQUIS 3047 JARDIANCE 2751 INVOKANA 2730 Prolia 2724 TRULICITY 2294 JANUVIA 2244 Name: Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1, dtype: int64 res KEYTRUDA 9568 Belviq 8493 TECFIDERA 3538 TRADJENTA 2782 Tresiba 2732 Non-Covered Product 2487 Sentus 2232 Vivitrol 380mg 2109 SAR231893 1888 JARDIANCE 1863 Name: Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1, dtype: int64 In common 1
This the type of drug associated with the payment,
Standardize the format and try to extract key element
conv=comb['Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1'].apply(lambda x:str(x).lower())#.value_counts()
conv=comb['Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1'].apply(lambda x:str(x).lower())
l=conv.value_counts()[:20]
comb_modify['Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M']=\
np.where([i in l.index for i in conv],conv,'Other')
check 'Product_Category_or_Therapeutic_Area_1'
top_cat("Product_Category_or_Therapeutic_Area_1")
gen Diabetes 10319 RESPIRATORY 9065 Cardiovascular & Metabolism 7788 Immunology 6023 CARDIOVASCULAR 5875 NEUROSCIENCE 5375 Endocrinology 5060 Cardiovascular and Metabolism 4865 GASTROENTEROLOGY 4661 Oncology 4316 Name: Product_Category_or_Therapeutic_Area_1, dtype: int64 res ONCOLOGY 18090 Nutrition and Weight Loss 8493 Oncology 7688 DIABETES 7604 Diabetes 6485 Cardiology/Vascular Diseases 5975 NEUROLOGY 5788 Immunology 3878 CNS 3803 CARDIOVASCULAR 3596 Name: Product_Category_or_Therapeutic_Area_1, dtype: int64 In common 4
conv=comb['Product_Category_or_Therapeutic_Area_1'].apply(lambda x:str(x).lower())
l=conv.value_counts()[:20]
comb_modify['Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M']=\
np.where([i in l.index for i in conv],conv,'Other')
check Therapeutic Area
comb_modify['Product_Category']=comb['Product_Category_or_Therapeutic_Area_1'].apply(lambda x:str(x).lower()).tolist()
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains('resp', regex=True), 'resp', comb_modify['Product_Category'])
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains('diab', regex=True), 'diab', comb_modify['Product_Category'])
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains('oncol', regex=True), 'oncol', comb_modify['Product_Category'])
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains('cardio', regex=True), 'cardio', comb_modify['Product_Category'])
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains('nero', regex=True), 'nero', comb_modify['Product_Category'])
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains('immun', regex=True), 'immun', comb_modify['Product_Category'])
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains('gastro', regex=True), 'gastro', comb_modify['Product_Category'])
comb_modify['Product_Category'] = np.where(comb_modify['Product_Category'].str.contains\
('resp|diab|oncol|cardio|nero|immun|gastro', regex=True), comb_modify['Product_Category'], 'other')
comb_modify['Product_Category'].value_counts()
other 252194 cardio 40909 oncol 38574 diab 29288 resp 16542 immun 14425 gastro 8066 Name: Product_Category, dtype: int64
top_cat('Recipient_Primary_Business_Street_Address_Line1')
gen 9500 EUCLID AVE 226 11100 EUCLID AVE 175 2500 N STATE ST 161 200 1ST ST SW 139 5323 HARRY HINES BLVD 128 10666 N TORREY PINES RD 124 100 E LANCASTER AVE 122 3800 RESERVOIR RD NW 114 6550 FANNIN ST 103 3400 SPRUCE ST 95 Name: Recipient_Primary_Business_Street_Address_Line1, dtype: int64 res 1 Triangle Dr 1838 12221 MERIT DR STE 500 920 619 SOUTH 19TH STREET 808 1515 HOLCOMBE BLVD 738 2301 COMMONWEALTH BLVD 532 450 BROOKLINE AVE 505 175 CROSS KEYS ROAD 477 11818 WILSHIRE BLVD 461 1211 MEDICAL CENTER DRIVE 440 3730 S EASTERN AVE 429 Name: Recipient_Primary_Business_Street_Address_Line1, dtype: int64 In common 0
the address field is hard to extract any information
top_cat('Submitting_Applicable_Manufacturer_or_Applicable_GPO_Name')
gen Janssen Pharmaceuticals, Inc 11108 Pfizer Inc. 10609 Allergan Inc. 9052 AstraZeneca Pharmaceuticals LP 8224 Novo Nordisk Inc 7504 Merck Sharp & Dohme Corporation 6763 Amgen Inc. 6076 Novartis Pharmaceuticals Corporation 5970 GlaxoSmithKline, LLC. 5838 Boehringer Ingelheim Pharmaceuticals, Inc. 5687 Name: Submitting_Applicable_Manufacturer_or_Applicable_GPO_Name, dtype: int64 res Pfizer Inc. 15205 Merck Sharp & Dohme Corporation 13370 Incyte Corporation 12030 Eisai Inc. 10933 Sanofi and Genzyme US Companies 10713 Boehringer Ingelheim Corporate Center GmbH 9723 Alkermes, Inc. 8919 AbbVie, Inc. 7543 Novo Nordisk Inc 6791 Genentech, Inc. 6687 Name: Submitting_Applicable_Manufacturer_or_Applicable_GPO_Name, dtype: int64 In common 3
GPO=comb['Submitting_Applicable_Manufacturer_or_Applicable_GPO_Name'].fillna('NA').apply(lambda x: x.lower().split(' ')[0])
L=GPO.value_counts()[:10].index
comb_modify['GPO_M']=[i if i in L else 'other' for i in GPO ]
comb_modify['GPO_M'].value_counts()
other 241971 pfizer 25655 merck 20426 sanofi 16674 boehringer 16564 janssen 16563 novo 14274 abbvie, 13110 incyte 12231 astrazeneca 11420 eisai 11110 Name: GPO_M, dtype: int64
comb_modify.to_csv('comb_modify.csv')
comb_modify=pd.read_csv('comb_modify.csv')
comb_modify['Total_Amount_of_Payment_USDollars']=np.log(comb['Total_Amount_of_Payment_USDollars'].tolist())
comb_modify.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 399998 entries, 0 to 399997 Data columns (total 20 columns): Unnamed: 0 399998 non-null int64 Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country 399998 non-null object Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State 399998 non-null object Covered_or_Noncovered_Indicator_1 399998 non-null object Dispute_Status_for_Publication 399998 non-null object Form_of_Payment_or_Transfer_of_Value 399998 non-null object Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 399998 non-null object Recipient_Country 399998 non-null object Recipient_State 399998 non-null object Related_Product_Indicator 399998 non-null object Total_Amount_of_Payment_USDollars 399998 non-null float64 dig1 399998 non-null object dig2 399998 non-null object dig3 399998 non-null object Applicable_Manufacturer_or_Applicable_GPO_M 399998 non-null int64 Payment_Weekday 399998 non-null int64 Payment_Month 399998 non-null int64 Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M 399998 non-null object Product_Category 399998 non-null object GPO_M 399998 non-null object dtypes: float64(1), int64(4), object(15) memory usage: 61.0+ MB
comb_modify.drop('Applicable_Manufacturer_or_Applicable_GPO_M',axis=1,inplace=True)
comb_modify.head()
| Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country | Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State | Covered_or_Noncovered_Indicator_1 | Dispute_Status_for_Publication | Form_of_Payment_or_Transfer_of_Value | Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 | Recipient_Country | Recipient_State | Related_Product_Indicator | Total_Amount_of_Payment_USDollars | dig1 | dig2 | dig3 | Payment_Weekday | Payment_Month | Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M | Product_Category | GPO_M | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | United States | TX | Covered | No | In-kind items and services | Drug | United States | PA | Yes | 2.438863 | 0 | 0 | 30 | 5 | 8 | Other | other | other |
| 1 | United States | FL | NA | No | Cash or cash equivalent | NA | United States | OR | No | 3.099642 | 0 | 0 | 0 | 6 | 4 | NA | other | other |
| 2 | United States | FL | NA | No | Cash or cash equivalent | NA | United States | CA | No | 4.759349 | 0 | 0 | 0 | 1 | 3 | NA | other | other |
| 3 | United States | TX | Covered | No | In-kind items and services | Drug | United States | NY | Yes | 2.573375 | 0 | 0 | 0 | 4 | 10 | Other | other | other |
| 4 | United States | TX | Covered | No | In-kind items and services | Drug | United States | PA | Yes | 2.778819 | 0 | 0 | 30 | 1 | 10 | Other | other | other |
comb_modify.drop('Unnamed: 0',axis=1,inplace=True )
comb_modify['dig1']=comb_modify['dig1'].astype(str)
comb_modify['dig2']=comb_modify['dig2'].astype(str)
comb_modify['dig3']=comb_modify['dig3'].astype(str)
comb_modify['Payment_Month']=comb_modify['Payment_Month'].astype(str)
comb_modify['Payment_Weekday']=comb_modify['Payment_Weekday'].astype(str)
comb_modify['Applicable_Manufacturer_or_Applicable_GPO_M']=comb_modify['Applicable_Manufacturer_or_Applicable_GPO_M'].astype(str)
X_trainval,X_test,y_trainval,y_test=train_test_split(comb_modify,comb['Tag'])
X_train,X_val,y_train,y_val=train_test_split(X_trainval,y_trainval)
comb_modify.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 399998 entries, 0 to 399997 Data columns (total 19 columns): Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country 399998 non-null object Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State 399998 non-null object Covered_or_Noncovered_Indicator_1 399998 non-null object Dispute_Status_for_Publication 399998 non-null object Form_of_Payment_or_Transfer_of_Value 399998 non-null object Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 399998 non-null object Recipient_Country 399998 non-null object Recipient_State 399998 non-null object Related_Product_Indicator 399998 non-null object Total_Amount_of_Payment_USDollars 399998 non-null float64 dig1 399998 non-null object dig2 399998 non-null object dig3 399998 non-null object Applicable_Manufacturer_or_Applicable_GPO_M 399998 non-null object Payment_Weekday 399998 non-null object Payment_Month 399998 non-null object Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M 399998 non-null object Product_Category 399998 non-null object GPO_M 399998 non-null object dtypes: float64(1), object(18) memory usage: 58.0+ MB
comb_modify.fillna('NA',inplace=True)
comb_modify.nunique()
Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country 23 Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State 47 Covered_or_Noncovered_Indicator_1 3 Dispute_Status_for_Publication 2 Form_of_Payment_or_Transfer_of_Value 5 Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 5 Recipient_Country 11 Recipient_State 58 Related_Product_Indicator 2 Total_Amount_of_Payment_USDollars 75059 dig1 10 dig2 10 dig3 10 Applicable_Manufacturer_or_Applicable_GPO_M 21 Payment_Weekday 7 Payment_Month 12 Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M 21 Product_Category 7 GPO_M 11 dtype: int64
Rerun log reg
X_trainval,X_test,y_trainval,y_test=train_test_split(comb_modify,comb['Tag'])
X_train,X_val,y_train,y_val=train_test_split(X_trainval,y_trainval)
cat
Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country True Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State True Covered_or_Noncovered_Indicator_1 True Dispute_Status_for_Publication True Form_of_Payment_or_Transfer_of_Value True Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 True Recipient_Country True Recipient_State True Related_Product_Indicator True Total_Amount_of_Payment_USDollars False dig1 True dig2 True dig3 True Applicable_Manufacturer_or_Applicable_GPO_M True Payment_Weekday True Payment_Month True Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M True Product_Category True GPO_M True dtype: bool
cat=comb_modify.dtypes=='object'
cont=comb_modify.dtypes!='object'
prep = ColumnTransformer([("OneHot", OneHotEncoder(handle_unknown='ignore'),cat), ('pt', 'passthrough',cont)])
log_pipe = Pipeline([('preprocessing', prep), ('LogReg',LogisticRegression(penalty='l1',solver='saga',C=1))])
log_pipe.fit(X_train,y_train)
C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\linear_model\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
Pipeline(memory=None,
steps=[('preprocessing',
ColumnTransformer(n_jobs=None, remainder='drop',
sparse_threshold=0.3,
transformer_weights=None,
transformers=[('OneHot',
OneHotEncoder(categorical_features=None,
categories=None,
drop=None,
dtype=<class 'numpy.float64'>,
handle_unknown='ignore',
n_values=None,
sparse=True),
Applicable_Manufacturer_or_Applicable_GPO_...
Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M False
Product_Category False
GPO_M False
dtype: bool)],
verbose=False)),
('LogReg',
LogisticRegression(C=1, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1,
l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None,
penalty='l1', random_state=None,
solver='saga', tol=0.0001, verbose=0,
warm_start=False))],
verbose=False)
#LogReg seems to have a fair result. Check the actual parameter for the model -- which variable plays a more important role
log_pipe.score(X_train,y_train)
0.944359505417826
log_pipe.score(X_test,y_test)
0.94271
len(log_pipe.named_steps['LogReg'].coef_[0])
261
cols = log_pipe.named_steps['preprocessing'].named_transformers_['OneHot'].get_feature_names().tolist() \
+ ['Total_Amount_of_Payment_USDollars']
len(cols)
261
a={i:j for i,j in zip(cols, log_pipe.named_steps['LogReg'].coef_[0])}
outcome=pd.Series(a)
for i ,j in enumerate(comb_modify.columns[cat]):
print(i,j)
0 Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country 1 Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State 2 Covered_or_Noncovered_Indicator_1 3 Dispute_Status_for_Publication 4 Form_of_Payment_or_Transfer_of_Value 5 Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 6 Recipient_Country 7 Recipient_State 8 Related_Product_Indicator 9 dig1 10 dig2 11 dig3 12 Applicable_Manufacturer_or_Applicable_GPO_M 13 Payment_Weekday 14 Payment_Month 15 Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M 16 Product_Category 17 GPO_M
outcome.sort_values()[:4]
(Index(['x12_100000000232', 'x12_100000000234', 'x12_100000000144', 'x10_152'], dtype='object'), x12_100000000232 -8.269934 x12_100000000234 -7.221079 x12_100000000144 -6.589039 x10_152 -3.001802 dtype: float64)
outcome.sort_values()[-4:]
x12_100000010579 4.736432 x12_100000000163 5.310122 x12_100000000067 6.660959 x17_boehringer 7.259178 dtype: float64
variables with most important power are those fine tuned vars
fig, ax = plt.subplots(1, 1, figsize=(25, 8))
plt.xticks(rotation='vertical')
sns.barplot(x=cols, y=log_pipe.named_steps['LogReg'].coef_[0])
plt.show()
#cross validation
param_grid_logr = {'LogReg__C': np.logspace(-3, 2, 6)}
grid_logr = GridSearchCV(log_pipe, param_grid_logr, cv=3, n_jobs=-1).fit(X_train, y_train)
res = pd.DataFrame(grid_logr.cv_results_)
res
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_LogReg__C | params | split0_test_score | split1_test_score | split2_test_score | mean_test_score | std_test_score | rank_test_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.518065 | 1.096841 | 0.759453 | 0.140694 | 0.001 | {'LogReg__C': 0.001} | 0.638533 | 0.639449 | 0.634755 | 0.637579 | 0.002031 | 6 |
| 1 | 10.574476 | 0.975294 | 0.802119 | 0.207989 | 0.01 | {'LogReg__C': 0.01} | 0.645133 | 0.646649 | 0.642262 | 0.644681 | 0.001819 | 5 |
| 2 | 11.488757 | 1.226887 | 0.781254 | 0.098168 | 0.1 | {'LogReg__C': 0.1} | 0.645480 | 0.647062 | 0.642329 | 0.644957 | 0.001967 | 4 |
| 3 | 13.069168 | 1.324641 | 0.790214 | 0.115983 | 1 | {'LogReg__C': 1.0} | 0.646120 | 0.646982 | 0.641875 | 0.644992 | 0.002232 | 3 |
| 4 | 12.728244 | 0.888214 | 0.777529 | 0.025456 | 10 | {'LogReg__C': 10.0} | 0.645800 | 0.646942 | 0.642595 | 0.645112 | 0.001840 | 1 |
| 5 | 9.895734 | 1.809515 | 0.485009 | 0.071907 | 100 | {'LogReg__C': 100.0} | 0.645787 | 0.646915 | 0.642622 | 0.645108 | 0.001817 | 2 |
fig, ax = plt.subplots(1, 1, figsize=(12,6))
##sns.lineplot(x='param_LogReg__C', y='mean_train_score', data=res, label='Train Score')
sns.lineplot(x='param_LogReg__C', y='mean_test_score', data=res, label='Test Score')
ax.set_xlabel('C')
plt.xscale("log")
plt.legend()
plt.show()
tree_pipe = Pipeline([('preprocessing', prep), ('Tree',DecisionTreeClassifier(
min_impurity_decrease=0.01,
min_samples_leaf=20, min_samples_split=20,
min_weight_fraction_leaf=0.001))])
tree_pipe.fit(X_train,y_train)
print("{0} training accuracy: {1:.3f} and test accuracy: {2:.3f}".\
format('tree',\
tree_pipe.score(X_train,y_train),tree_pipe.score(X_test,y_test)))
tree training accuracy: 0.848 and test accuracy: 0.847
tree_dot = export_graphviz(tree_pipe.named_steps['Tree'], out_file=None, feature_names=cols)
graph = graphviz.Source(tree_dot)
graph.render(cleanup=True)
graph
tree_pipe = Pipeline([('preprocessing', prep), ('Tree',DecisionTreeClassifier(
min_impurity_decrease=0.01,
min_samples_leaf=20, min_samples_split=20,
min_weight_fraction_leaf=0.001))])
tree_pipe.fit(X_train,y_train)
print("{0} training accuracy: {1:.3f} and test accuracy: {2:.3f}".\
format('tree',\
tree_pipe.score(X_train,y_train),tree_pipe.score(X_test,y_test)))
tree_dot = export_graphviz(tree_pipe.named_steps['Tree'], out_file=None, feature_names=cols)
graph = graphviz.Source(tree_dot)
graph.render(cleanup=True)
graph
for i,j in zip(cols,tree_pipe.named_steps['Tree'].feature_importances_):
if j!=0:
print(i,j)
x1_NA 0.04277439647415692 x12_100000000193 0.03719061339729068 x12_100000000263 0.05197537224759333 x15_Other 0.04580634261572859 Total_Amount_of_Payment_USDollars 0.8222532752652304
# try RF and grid search for parameter tuning
voting = VotingClassifier(
[('logreg', LogisticRegression(C=100)),
('tree', DecisionTreeClassifier(max_depth=3, random_state=0))],
voting='soft')
voting_pipe = Pipeline([('preprocessing', prep), ('Volting',voting)])
X_train.shape
(224998, 19)
X_train.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 224998 entries, 75524 to 74228 Data columns (total 19 columns): Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_Country 224998 non-null object Applicable_Manufacturer_or_Applicable_GPO_Making_Payment_State 224998 non-null object Covered_or_Noncovered_Indicator_1 224998 non-null object Dispute_Status_for_Publication 224998 non-null object Form_of_Payment_or_Transfer_of_Value 224998 non-null object Indicate_Drug_or_Biological_or_Device_or_Medical_Supply_1 224998 non-null object Recipient_Country 224998 non-null object Recipient_State 224998 non-null object Related_Product_Indicator 224998 non-null object Total_Amount_of_Payment_USDollars 224998 non-null float64 dig1 224998 non-null object dig2 224998 non-null object dig3 224998 non-null object Applicable_Manufacturer_or_Applicable_GPO_M 224998 non-null object Payment_Weekday 224998 non-null object Payment_Month 224998 non-null object Name_of_Drug_or_Biological_or_Device_or_Medical_Supply_1_M 224998 non-null object Product_Category 224998 non-null object GPO_M 224998 non-null object dtypes: float64(1), object(18) memory usage: 34.3+ MB
voting.fit(X_train, y_train)
voting.score(X_test, y_test)
C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning)
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-105-ad0ee6bd94af> in <module> ----> 1 voting.fit(X_train, y_train) 2 3 voting.score(X_train, y_train), voting.score(X_test, y_test) ~\Anaconda3\lib\site-packages\sklearn\ensemble\voting.py in fit(self, X, y, sample_weight) 277 transformed_y = self.le_.transform(y) 278 --> 279 return super().fit(X, transformed_y, sample_weight) 280 281 def predict(self, X): ~\Anaconda3\lib\site-packages\sklearn\ensemble\voting.py in fit(self, X, y, sample_weight) 99 delayed(_parallel_fit_estimator)(clone(clf), X, y, 100 sample_weight=sample_weight) --> 101 for clf in clfs if clf not in (None, 'drop') 102 ) 103 ~\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable) 1001 # remaining jobs. 1002 self._iterating = False -> 1003 if self.dispatch_one_batch(iterator): 1004 self._iterating = self._original_iterator is not None 1005 ~\Anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator) 832 return False 833 else: --> 834 self._dispatch(tasks) 835 return True 836 ~\Anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch) 751 with self._lock: 752 job_idx = len(self._jobs) --> 753 job = self._backend.apply_async(batch, callback=cb) 754 # A job can complete so quickly than its callback is 755 # called before we get here, causing self._jobs to ~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback) 199 def apply_async(self, func, callback=None): 200 """Schedule a func to be run""" --> 201 result = ImmediateResult(func) 202 if callback: 203 callback(result) ~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch) 580 # Don't delay the application, to avoid keeping the input 581 # arguments in memory --> 582 self.results = batch() 583 584 def get(self): ~\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self) 254 with parallel_backend(self._backend, n_jobs=self._n_jobs): 255 return [func(*args, **kwargs) --> 256 for func, args, kwargs in self.items] 257 258 def __len__(self): ~\Anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0) 254 with parallel_backend(self._backend, n_jobs=self._n_jobs): 255 return [func(*args, **kwargs) --> 256 for func, args, kwargs in self.items] 257 258 def __len__(self): ~\Anaconda3\lib\site-packages\sklearn\ensemble\voting.py in _parallel_fit_estimator(estimator, X, y, sample_weight) 41 raise 42 else: ---> 43 estimator.fit(X, y) 44 return estimator 45 ~\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight) 1530 1531 X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order="C", -> 1532 accept_large_sparse=solver != 'liblinear') 1533 check_classification_targets(y) 1534 self.classes_ = np.unique(y) ~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator) 717 ensure_min_features=ensure_min_features, 718 warn_on_dtype=warn_on_dtype, --> 719 estimator=estimator) 720 if multi_output: 721 y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False, ~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator) 494 try: 495 warnings.simplefilter('error', ComplexWarning) --> 496 array = np.asarray(array, dtype=dtype, order=order) 497 except ComplexWarning: 498 raise ValueError("Complex data not supported\n" ~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order) 83 84 """ ---> 85 return array(a, dtype, copy=False, order=order) 86 87 ValueError: could not convert string to float: 'sanofi'
feature selection might be slow for current setting
def drop_feature_importance(est, X, y):
base_score = np.mean(cross_val_score(est, X, y, cv=5))
scores = []
for feature in range(X.shape[1]):
mask = np.ones(X.shape[1], 'bool')
mask[feature] = False
X_new = X[:, mask]
this_score = np.mean(cross_val_score(est, X_new, y, cv=5))
scores.append(base_score - this_score)
return np.array(scores)
#try rf
gbrt = GradientBoostingRegressor().fit(X_train, y_train)
fig, axs = plot_partial_dependence(
gbrt, X_train, np.argsort(gbrt.feature_importances_)[-6:],
feature_names=boston.feature_names, n_jobs=3,
grid_resolution=50)
# get rid of pipline and convert all predictor in advance
ohc= OneHotEncoder(handle_unknown='ignore')
ohc.fit(comb_modify.drop("Total_Amount_of_Payment_USDollars",axis=1))
OneHotEncoder(categorical_features=None, categories=None, drop=None,
dtype=<class 'numpy.float64'>, handle_unknown='ignore',
n_values=None, sparse=True)
desc=ohc.transform(comb_modify.drop("Total_Amount_of_Payment_USDollars",axis=1))
ohc.categories_
[array(['Austria', 'Barbados', 'Belgium', 'Brazil', 'Canada', 'Denmark',
'Finland', 'France', 'Germany', 'Great Britain (Uk)', 'Iceland',
'Ireland', 'Israel', 'Japan', 'Korea (Republic of)', 'Netherlands',
'New Zealand', 'Norway', 'Spain', 'Sweden', 'Switzerland',
'United Arab Emirates', 'United States'], dtype=object),
array(['AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'IA',
'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'MI', 'MN', 'MO',
'MS', 'MT', 'NA', 'NC', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK',
'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT',
'WA', 'WI', 'WV'], dtype=object),
array(['Covered', 'NA', 'Non-Covered'], dtype=object),
array(['No', 'Yes'], dtype=object),
array(['Any other ownership interest', 'Cash or cash equivalent',
'Dividend, profit or other return on investment',
'In-kind items and services', 'Stock'], dtype=object),
array(['Biological', 'Device', 'Drug', 'Medical Supply', 'NA'],
dtype=object),
array(['Australia', 'Belgium', 'Canada', 'Denmark', 'Germany',
'Great Britain (Uk)', 'Japan', 'Mexico', 'NA', 'United States',
'United States Minor Outlying Islands'], dtype=object),
array(['AA', 'AE', 'AK', 'AL', 'AP', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC',
'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA',
'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NA', 'NC', 'ND',
'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR',
'PW', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VI', 'VT', 'WA',
'WI', 'WV', 'WY'], dtype=object),
array(['No', 'Yes'], dtype=object),
array(['0', '169', '24', '31', '50458', '597', '6', '62856', '69', '78'],
dtype=object),
array(['0', '140', '152', '3029', '4060', '529', '580', '6', '62', '710'],
dtype=object),
array(['0', '1', '10', '15', '2', '20', '3', '30', '60', '90'],
dtype=object),
array(['0', '100000000053', '100000000067', '100000000089',
'100000000108', '100000000136', '100000000144', '100000000146',
'100000000163', '100000000193', '100000000203', '100000000204',
'100000000228', '100000000232', '100000000234', '100000000255',
'100000000263', '100000000278', '100000000286', '100000005449',
'100000010579'], dtype=object),
array(['0', '1', '2', '3', '4', '5', '6'], dtype=object),
array(['1', '10', '11', '12', '2', '3', '4', '5', '6', '7', '8', '9'],
dtype=object),
array(['NA', 'Other', 'biooncology',
'cardiac arrhythmias and heart failure',
'cardiology/vascular diseases', 'cardiovascular',
'cardiovascular & metabolism', 'cardiovascular and metabolism',
'cns', 'dermatology', 'diabetes', 'endocrinology',
'gastroenterology', 'hiv', 'immunology', 'neurology',
'neuroscience', 'nutrition and weight loss', 'oncology',
'ophthalmology', 'respiratory'], dtype=object),
array(['cardio', 'diab', 'gastro', 'immun', 'oncol', 'other', 'resp'],
dtype=object),
array(['abbvie,', 'astrazeneca', 'boehringer', 'eisai', 'incyte',
'janssen', 'merck', 'novo', 'other', 'pfizer', 'sanofi'],
dtype=object)]
col_ohc=ohc.get_feature_names().tolist()
ohc.categorical_features
col_ohc.append('spend')
desc_df=pd.DataFrame(desc.todense(),columns=col_ohc[:-1])
desc_df.head()
| x0_Austria | x0_Barbados | x0_Belgium | x0_Brazil | x0_Canada | x0_Denmark | x0_Finland | x0_France | x0_Germany | x0_Great Britain (Uk) | ... | x17_astrazeneca | x17_boehringer | x17_eisai | x17_incyte | x17_janssen | x17_merck | x17_novo | x17_other | x17_pfizer | x17_sanofi | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
5 rows × 265 columns
comb_modify[["Total_Amount_of_Payment_USDollars"]].head()
| Total_Amount_of_Payment_USDollars | |
|---|---|
| 0 | 2.438863 |
| 1 | 3.099642 |
| 2 | 4.759349 |
| 3 | 2.573375 |
| 4 | 2.778819 |
desc_df['log_spend']=comb_modify["Total_Amount_of_Payment_USDollars"]
desc_df.head()
| x0_Austria | x0_Barbados | x0_Belgium | x0_Brazil | x0_Canada | x0_Denmark | x0_Finland | x0_France | x0_Germany | x0_Great Britain (Uk) | ... | x17_boehringer | x17_eisai | x17_incyte | x17_janssen | x17_merck | x17_novo | x17_other | x17_pfizer | x17_sanofi | log_spend | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.438863 |
| 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 3.099642 |
| 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.759349 |
| 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.573375 |
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.778819 |
5 rows × 266 columns
X_trainval,X_test,y_trainval,y_test=train_test_split(desc_df,comb['Tag'])
X_train,X_val,y_train,y_val=train_test_split(X_trainval,y_trainval)
gbrt = GradientBoostingRegressor().fit(X_train, y_train)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-153-0cb388d0b24b> in <module> 1 gbrt = GradientBoostingRegressor().fit(X_train, y_train) ----> 2 fig, axs = plot_partial_dependence( 3 gbrt, X_train, np.argsort(gbrt.feature_importances_)[-6:], 4 feature_names=col_ohc, n_jobs=3, 5 grid_resolution=50) NameError: name 'plot_partial_dependence' is not defined
gbrt.score(X_test,y_test)
0.8180253588223776
grid_gbrt = {'LogReg__C': np.logspace(-3, 2, 6)}
grid_logr = GridSearchCV(log_pipe, param_grid_logr, cv=3, n_jobs=-1).fit(X_train, y_train)
xbrt = xgb().fit(X_train, y_train)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-162-4828141d16aa> in <module> ----> 1 xbrt = xgb().fit(X_train, y_train) TypeError: 'module' object is not callable
dtrain = xgb.DMatrix(X_train, label=y_train)
C:\Users\Yilun\Anaconda3\lib\site-packages\xgboost\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version if getattr(data, 'base', None) is not None and \
dtest = xgb.DMatrix(X_test, label=y_test)
dtrain = xgb.DMatrix(X_train, label=y_train)
param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
param['nthread'] = 4
param['eval_metric'] = 'auc'
# You can also specify multiple eval metrics:
param['eval_metric'] = ['auc', 'ams@0']
# alternatively:
# plst = param.items()
# plst += [('eval_metric', 'ams@0')]
# Specify validations set to watch performance
evallist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 10
bst = xgb.train(param, dtrain, num_round, evallist)
[0] eval-auc:0.907643 eval-ams@0:357.311 train-auc:0.904592 train-ams@0:530.27 [1] eval-auc:0.950966 eval-ams@0:388.696 train-auc:0.949044 train-ams@0:576.218 [2] eval-auc:0.962695 eval-ams@0:412.558 train-auc:0.961391 train-ams@0:612.6 [3] eval-auc:0.966003 eval-ams@0:415.845 train-auc:0.96473 train-ams@0:618.996 [4] eval-auc:0.970072 eval-ams@0:427.962 train-auc:0.969082 train-ams@0:632.899 [5] eval-auc:0.971385 eval-ams@0:427.823 train-auc:0.970332 train-ams@0:636.149 [6] eval-auc:0.974157 eval-ams@0:439.338 train-auc:0.973105 train-ams@0:653.457 [7] eval-auc:0.975736 eval-ams@0:443.489 train-auc:0.974735 train-ams@0:661.01 [8] eval-auc:0.976948 eval-ams@0:447.204 train-auc:0.976001 train-ams@0:666.378 [9] eval-auc:0.977948 eval-ams@0:452.959 train-auc:0.977057 train-ams@0:675.362
bst.save_model('0001.model')
#The model and its feature map can also be dumped to a text file.
# dump model
bst.dump_model('dump.raw.txt')
# dump model with feature map
bst.dump_model('dump.raw.txt', 'featmap.txt')
#A saved model can be loaded as follows:
#bst = xgb.Booster({'nthread': 4}) # init model
#bst.load_model('model.bin') # load data
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-170-8746d7e5604d> in <module> 5 bst.dump_model('dump.raw.txt') 6 # dump model with feature map ----> 7 bst.dump_model('dump.raw.txt', 'featmap.txt') 8 #A saved model can be loaded as follows: 9 ~\Anaconda3\lib\site-packages\xgboost\core.py in dump_model(self, fout, fmap, with_stats, dump_format) 1391 else: 1392 need_close = False -> 1393 ret = self.get_dump(fmap, with_stats, dump_format) 1394 if dump_format == 'json': 1395 fout.write('[\n') ~\Anaconda3\lib\site-packages\xgboost\core.py in get_dump(self, fmap, with_stats, dump_format) 1443 else: 1444 if fmap != '' and not os.path.exists(fmap): -> 1445 raise ValueError("No such file: {0}".format(fmap)) 1446 _check_call(_LIB.XGBoosterDumpModelEx(self.handle, 1447 c_str(fmap), ValueError: No such file: featmap.txt
xgb.plot_importance(bst)
<matplotlib.axes._subplots.AxesSubplot at 0x25b07188ac8>
the ensamble model did yield to a much better result compared with single models
and the feature importance reflected of what we saw in the single model result
try cv for svm
clf = svm.SVC()
clf.train(X_train,y_train)
grid
clf=RandomForestClassifier()
Leverage random search to find parameters using random forest
from scipy.stats import randint
param_dist = {"max_depth": [3, None],
"max_features": randint(1, 11),
"min_samples_split": randint(2, 11),
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
n_iter_search = 20
random_search = RandomizedSearchCV(clf,
param_distributions=param_dist,
n_iter=200, cv=5)
random_search.fit(X_train,y_train)
C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) C:\Users\Yilun\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. 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RandomizedSearchCV(cv=5, error_score='raise-deprecating',
estimator=RandomForestClassifier(bootstrap=True,
class_weight=None,
criterion='gini',
max_depth=None,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators='warn',
n_jobs=None,
oob_sc...
param_distributions={'bootstrap': [True, False],
'criterion': ['gini', 'entropy'],
'max_depth': [3, None],
'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000025B04732048>,
'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000025B0720B860>},
pre_dispatch='2*n_jobs', random_state=None, refit=True,
return_train_score=False, scoring=None, verbose=0)
RandomizedSearchCV(cv=5, error_score='raise-deprecating', estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None, oob_sc... param_distributions={'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'max_depth': [3, None], 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000025B04732048>, 'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000025B0720B860>}, pre_dispatch='2*n_jobs', random_state=None, refit=True, return_train_score=False, scoring=None, verbose=0)
random_search.best_params_
{'bootstrap': False,
'criterion': 'gini',
'max_depth': None,
'max_features': 10,
'min_samples_split': 5}
random_search.best_estimator_
RandomForestClassifier(bootstrap=False, class_weight=None, criterion='gini',
max_depth=None, max_features=10, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, n_estimators=10,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
random_search.cv_results_['mean_test_score'][random_search.cv_results_['rank_test_score']-1][:10]
array([0.96818638, 0.9688575 , 0.87053663, 0.96403524, 0.96813305,
0.85494093, 0.96748416, 0.87663446, 0.7940115 , 0.95988853])
random_search.cv_results_['rank_test_score'][:10]-1
array([171, 119, 167, 103, 38, 170, 140, 195, 73, 105])
for i in random_search.cv_results_['rank_test_score'][:10]-1:
print(random_search.cv_results_['params'][i])
{'bootstrap': True, 'criterion': 'gini', 'max_depth': None, 'max_features': 9, 'min_samples_split': 5}
{'bootstrap': False, 'criterion': 'entropy', 'max_depth': None, 'max_features': 8, 'min_samples_split': 9}
{'bootstrap': True, 'criterion': 'entropy', 'max_depth': 3, 'max_features': 10, 'min_samples_split': 9}
{'bootstrap': True, 'criterion': 'entropy', 'max_depth': None, 'max_features': 5, 'min_samples_split': 7}
{'bootstrap': True, 'criterion': 'entropy', 'max_depth': None, 'max_features': 9, 'min_samples_split': 5}
{'bootstrap': False, 'criterion': 'gini', 'max_depth': 3, 'max_features': 7, 'min_samples_split': 3}
{'bootstrap': False, 'criterion': 'entropy', 'max_depth': None, 'max_features': 7, 'min_samples_split': 2}
{'bootstrap': True, 'criterion': 'gini', 'max_depth': 3, 'max_features': 8, 'min_samples_split': 5}
{'bootstrap': True, 'criterion': 'entropy', 'max_depth': 3, 'max_features': 3, 'min_samples_split': 7}
{'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 1, 'min_samples_split': 3}
model = random_search.best_estimator_
X_train.columns
Index(['x0_Austria', 'x0_Barbados', 'x0_Belgium', 'x0_Brazil', 'x0_Canada',
'x0_Denmark', 'x0_Finland', 'x0_France', 'x0_Germany',
'x0_Great Britain (Uk)',
...
'x17_boehringer', 'x17_eisai', 'x17_incyte', 'x17_janssen', 'x17_merck',
'x17_novo', 'x17_other', 'x17_pfizer', 'x17_sanofi', 'log_spend'],
dtype='object', length=266)
# get the encoded col_names
cols = X_train.columns.tolist()
# get the ordinal vars
# add the continuous vars
model_res = pd.DataFrame(data={'cols': cols,
'feature_importances': model.feature_importances_})
model_res.sort_values(by='feature_importances', ascending=False, inplace=True)
fig, ax = plt.subplots(1, 1, figsize=(20, 5))
sns.barplot(x='cols', y='feature_importances', data=model_res, ax=ax, color='lightblue')
plt.xticks(rotation='vertical')
plt.tight_layout()
plt.show()
tree=DecisionTreeClassifier( criterion= 'gini', max_depth=None, max_features=9, min_samples_split=5)
tree.fit(X_train,y_train)
print("{0} training accuracy: {1:.3f} and test accuracy: {2:.3f}".\
format('tree',\
tree.score(X_train,y_train),tree.score(X_test,y_test)))
tree_dot = export_graphviz(tree, out_file=None, feature_names=cols)
graph = graphviz.Source(tree_dot)
graph.render(cleanup=True)
graph
tree training accuracy: 0.981 and test accuracy: 0.950